If you’ve ever skipped over`the results section of a medical paper because terms like “confidence interval” or “p-value” go over your head, then you’re in the right place. You may be a clinical practitioner reading research articles to keep up-to-date with developments in your field or a medical student wondering how to approach your own research. Greater confidence in understanding statistical analysis and the results can benefit both working professionals and those undertaking research themselves.
If you are simply interested in properly understanding the published literature or if you are embarking on conducting your own research, this course is your first step. It offers an easy entry into interpreting common statistical concepts without getting into nitty-gritty mathematical formulae. To be able to interpret and understand these concepts is the best way to start your journey into the world of clinical literature. That’s where this course comes in - so let’s get started!
The course is free to enroll and take. You will be offered the option of purchasing a certificate of completion which you become eligible for, if you successfully complete the course requirements. This can be an excellent way of staying motivated! Financial Aid is also available.

AA

A great introduction to understanding research and a great platform to springboard keen clinicians into performing their own research. Will take what I've learnt and apply it to my own research!

DS

May 27, 2018

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I'm very new at this theme, this course has being the perfect beginning. If you don't have a mathematical background and you don't understand when the funny S appear, this is the course for you!

From the lesson

Building an intuitive understanding of statistical analysis

There is hardly any healthcare professional who is unfamiliar with the p-value. It is usually understood to have a watershed value of 0.05. If a research question is evaluated through the collection of data points and statistical analysis reveals a value less that 0.05, we accept this a proof that some significant difference was found, at least statistically.In reality things are a bit more complicated than that. The literature is currently full of questions about the ubiquitous p-vale and why it is not the panacea many of us have used it as. During this week you will develop an intuitive understanding of concept of a p-value. From there, I'll move on to the heart of probability theory, the Central Limit Theorem and data distribution.

Taught By

Juan H Klopper

Dr

Transcript

Now in a previous lesson we looked at the area under a curve, geometrical area and we saw how that related to a p value. We drew beautiful nice symmetric little graph. It colored in 2.5%, open, 0.25 on either side. We could also do the whole open 0.5 on one side. If we got some data points, we got some analysis, the results of those analyses would form some way we could calculate what the area under the curve was for that analysis. But anyone who's dealt with any kind of data points know, it's very rare to find data that looks like that. This is what you're more likely to see if you were to make a histogram of the finding of some data points for a value of some variable in the patients in the ward or in the clinic. this is the kind of spread of data that you are going to see. There is nothing symmetrical about that, there is certainly no beautiful little curve that we can draw over that that looks bell shaped, in any form or fashion. So how do we relate this, our actual findings, this is what you will find most of the time to this beautiful, symmetric bell shaped curve? Well, the Central limit theorem is going to come to our rescue. It's a mathematical theorem, and it's absolutely beautiful. Not gonna do the mess of it, but the beauty really is, is wonderful and deep, and I want you to have a look at it. Before we get there though, there's some terms that we have to discuss. Let's briefly mention them, that would be skewness kurtosis because that explains those ugly spread of data points that you're going to see. And then we're going to talk about combinations.

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